Content

Installation manual without LP launched

Glossary

Term Meaning
Aspect angle Head rotation degree (in degrees) on each of the three axes (up/down tilt relative to the horizontal axis; left/right tilt, relative to the vertical axis; a rotation about the vertical axis).
Batch Group of data processed simultaneously.
Bestshot The frame of the video stream on which the face/body is fixed in the optimal angle for further processing.
Detection FaceStream entity that contains the coordinates of face or body and the estimated value of the object that determines the bestshot.
Descriptor A set of unique features received from the warp. A descriptor requires much less storage memory in comparison with the sample and is used for comparison of faces.
Event LUNA PLATFORM entity, which contains information (city, user data, track id, etc.) about one face and/or body. This information is transferred to the LUNA PLATFORM by the FaceStream application. For a complete list of the transferred information, see the OpenAPI LUNA PLATFORM documentation.
LUNA Streams Service for creating and managing streams that contain policies for processing a video stream/video file/set of images.
Normalized image, warp Images containing a face or body and corresponding to VisionLabs standard. Used when working with LUNA PLATFORM.
Portrait Image of face or body that has been transformed to a specific format. The portrait has two types - “warp” (the image is transformed into warp format), “gost” (detection is cut out from the source frame, considering indentation).
Track Information about object’s position (face of a person) in a sequence of frames. If the object leaves the frame zone, the track doesn’t discontinue right away. For some time, the system expects the object to return and if it does, the track continues.
Tracking Object (face) tracking function in the frame sequence.
Abbreviation Term
DB Database
LP LUNA PLATFORM

System requirements

FaceStream is delivered in Docker containers and can be launched on CPU and GPU. Docker images of the containers are required for the installation. Internet connection is required on the server for Docker images download, or the images should be downloaded on any other device and moved to the server. It is required to manually specify login and password for Docker images downloading.

FaceStream can be launched with a Docker Compose script.

The following Docker and Docker Compose versions are recommended for FaceStream launching:

FaceStream and LUNA Streams containers launch were tested on the following operating systems:

The following OS is used inside the FaceStream container:

LP services and third-party applications

FaceStream requires LUNA PLATFORM components, additional databases, and the LUNA Streams service. Basic information about this software is contained in this document.

LUNA Streams is not a component of the LUNA PLATFORM.

The following LUNA PLATFORM components are used by default with FaceStream:

The following database versions are recommended for use with LUNA Streams:

To upload settings to the LUNA Configurator service, Python version 2.x or 3.x is required.

Installation and configuration of Oracle is not described in this manual. Further in the document, examples of launching using PostgreSQL will be given.

Balancers (for example, Nginx) and other software can be used when scaling the system to provide fail-safety. Their configuration is not described in this document.

Processors

Below are the requirements to launch FaceStream in a minimal configuration. System requirements for the production system are calculated based on the intended system load.

CPU

The following minimum requirements are given per FaceStream instance.

For the application to work correctly, the hardware must meet the following minimum requirements:

Hardware requirements can be affected by several factors:

Hardware should be selected based on the above factors.

FaceStream can also work in the computation speedup mode due to usage of video card resources or AVX2 instructions. CPU with AVX2 support is required. The system automatically detects available instructions and runs best performance.

GPU

GPU calculations are supported for FaceDetV3 only. See “defaultDetectorType” parameter in the FaceEngine configuration (“faceengine.conf”).

A minimum of 6GB or dedicated video RAM is required. 8 GB or more VRAM recommended.

Pascal, Volta, Turing architectures are supported.

Compute Capability 6.1 or higher and CUDA 11.4 are required.

The recommended NVIDIA driver is r470.

Now only one video card is supported per FaceStream instance.

Introduction

This document describes:

Before launching, it is necessary to explore the general information and sequence of actions.

FaceStream licensing is managed by a special parameter of the LUNA PLATFORM 5 license key, which determines the number of streams for LUNA Streams operation. Thus, a LUNA PLATFORM 5 license is required for FaceStream to work.

This manual provides an example of activating the LUNA PLATFORM license.

The Docker Compose scenario from this distribution is used for deploying the LUNA Streams and FaceStream on a single server.

It is considered that launching is performed on the server with CentOS OS, where FaceStream was not installed.

Firewall and SELinux should be manually configured on the server by the administrator. Their configuration is not described in this document.

This document does not include a tutorial for Docker usage. Please refer to the Docker documentation to find more information about Docker:

https://docs.docker.com

This document includes examples of FaceStream deployment in a minimal power operating for demonstration purposes and cannot be used for the production system.

All the provided commands should be executed using the Bash shell (when you launch commands directly on the server) or in a program for working with network protocols (when you remotely connect to the server), for example, Putty.

For more information on general operation and application settings, see the FaceStream administrator manual.

Before launch

Make sure you are the root user before launch!

Before launch FaceStream, you need to do the following:

  1. Unpack the FaceStream distribution
  2. Create symbolic link
  3. Install Docker
  4. Install Docker Compose if you plan to start FaceStream using Docker Compose script
  5. Choose logging method
  6. Set up GPU computing if you plan to use GPU
  7. Login to VisionLabs registry
  8. Activate license key
  9. Launch Influx OSS 2 container
  10. Launch PostgreSQL container
  11. Create and initialize database for LUNA Configurator
  12. Launch LUNA Configurator container
  13. Launch LUNA Licenses container

After the steps have been performed, you can start manually or automatically launching LUNA Streams and FaceStream.

Unpack distribution

It is recommended to move the archive to a pre-created directory for FaceStream and unpack the archive there.

The following commands should be performed under the root user.

Create a directory for FaceStream.

mkdir -p /var/lib/fs

Move the archive to the created directory. It is considered that the archive is saved to the “/root” directory.

mv /root/facestream_docker_v.5.1.27.zip /var/lib/fs/

Go to the directory.

cd /var/lib/fs/

Install the unzip utility if it is not installed.

yum install unzip

Unpack the archive.

unzip facestream_docker_v.5.1.27.zip

Create a symbolic link. The link indicates that the current version of the distribution file is used to run the software package.

ln -s facestream_docker_v.5.1.27 fs-current

Install Docker

Docker is required for launching of the FaceStream container.

The Docker installation is described in the official documentation:

https://docs.docker.com/engine/install/centos/.

You do not need to install Docker if you already have an installed Docker 20.10.8 on your server. Not guaranteed to work with higher versions of Docker.

Quick installation commands are listed below.

Check the official documentation for updates if you have any problems with the installation.

Install dependencies.

yum install -y yum-utils device-mapper-persistent-data lvm2

Add repository.

yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce.repo

Install Docker.

yum -y install docker-ce docker-ce-cli containerd.io

Launch Docker.

systemctl start docker
systemctl enable docker

Check Docker status.

systemctl status docker

Install Docker Compose

Note. Install Docker Compose only if you are going to use the FaceStream automatic launching script.

Install Docker Compose.

curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
chmod +x /usr/local/bin/docker-compose
ln -s /usr/local/bin/docker-compose /usr/bin/docker-compose

See the official documentation for details:

https://docs.docker.com/compose/install/

Choose logging method

There are two methods to output logs

Log output settings for LUNA PLATFORM services and LUNA Streams service are set in section <SERVICE_NAME>_LOGGER of LUNA Configurator service.

Log output settings for FaceStream are set in the settings logging of section FACE_STREAM_CONFIG of LUNA Configurator service.

If necessary, you can use both methods of displaying logs.

Logging to stdout

This method is used by default and requires no further action.

It is recommended to configure Docker log rotation to limit log sizes (see “Docker log rotation”).

Logging to file

Note. When you enable saving logs to a file, you should remember that logs occupy a certain place in the storage, and the process of logging to a file negatively affects system performance.

To use this method, you need to perform the following additional actions:

In the Docker Compose script, synchronization of directories with folders is not configured. You need to manually add folder mounting to the docker-compose.yml file.

See the instructions for enabling logging to files in the “Logging to server” section.

Install GPU dependencies

Skip this section if you are not going to utilize GPU for your calculations.

You need to install NVIDIA Container Toolkit to use GPU with Docker containers.

The example of the installation is given below.

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.repo | tee /etc/yum.repos.d/nvidia-docker.repo
yum install -y nvidia-container-toolkit
systemctl restart docker

Check the NVIDIA Container toolkit operating by running a base CUDA container (this container is not provided in the FaceStream distribution and should be downloaded from the Internet):

docker run --rm --gpus all nvidia/cuda:11.4-base nvidia-smi

See the documentation for additional information:

https://github.com/NVIDIA/nvidia-docker#centos-7x8x-docker-ce-rhel-7x8x-docker-ce-amazon-linux-12.

Attributes extraction on the GPU is engineered for maximum throughput. The input images are processed in batches. This reduces computation cost per image but does not provide the shortest latency per image.

GPU acceleration is designed for high load applications where request counts per second consistently reach thousands. It won’t be beneficial to use GPU acceleration in non-extensively loaded scenarios where latency matters.

Actions to launch FaceStream with GPU through Docker Compose

To launch FaceStream with GPU through Docker Compose, it is necessary, in addition to the above actions, to add the deploy section in the handlers field to the docker-compose.yml file.

vi /var/lib/fs/fs-current/example-docker/docker-compose.yml
  facestream:
    image: ${REGISTRY_ADDRESS}:${DOCKER_REGISTRY_PORT}/facestream:${FS_VER}
    deploy:
      resources:
        reservations:
          devices:
          - driver: nvidia
            count: all
            capabilities: [gpu]
    restart: always
    environment:
      CONFIGURATOR_HOST: ${HOST_CONFIGURATOR}
      CONFIGURATOR_PORT: 5070

driver - this field specifies the driver for the reserved device(s);

count - this field specifies the number of GPU devices that should be reserved (providing the host holds that number of GPUs);

capabilities - this field expresses both generic and driver specific capabilities. It must be set, otherwise, an error will be returned when deploying the service.

See the documentation for additional information:

https://docs.docker.com/compose/gpu-support/#enabling-gpu-access-to-service-containers.

Login to registry

When launching containers, you should specify a link to the image required for the container launching. This image will be downloaded from the VisionLabs registry. Before that, you should login to the registry.

Login and password can be requested from the VisionLabs representative.

Enter login <username>.

docker login dockerhub.visionlabs.ru --username <username>

After running the command, you will be prompted for a password. Enter password.

In the docker login command, you can enter the login and password at the same time, but this does not guarantee security because the password can be seen in the command history.

License activation

To activate/upgrade a license, follow the steps in the license activation manual included in the distribution package.

After that, follow the steps described below.

Specify license settings

For the HASP key, you need to specify the IP address of the licensing server. It can be set in one of two ways:

Choose the most convenient method and follow the steps described in the relevant sections.

Specify HASP license settings using dump file

Open the “platform_settings.json” file:

vi /var/lib/fs/fs-current/extras/conf/configurator_configs/platform_settings.json

Set the server IP address with your HASP key in the “server_address” field:

{
    "value": {
        "vendor": "hasp",
        "server_address": "127.0.0.1"
    },
    "description":"License vendor config",
    "name":"LICENSE_VENDOR",
    "tags":[]
},

Save the file.

If the license is activated using the HASP key, then two parameters “vendor” and “server_address” must be specified. If you want to change the HASP protection to Guardant, then you need to add the “license_id” field.

Specify license settings

For the Guardant key, you need to specify the IP address of the licensing server and the license ID. The settings can be set in one of two ways:

Choose the most convenient method and follow the steps described in the relevant sections.

Specify Guardant license settings using dump file

Open the file “platform_settings.json”:

vi /var/lib/fs/fs-current/extras/conf/configurator_configs/platform_settings.json

Enter the following data:

{
    "value": {
        "vendor": "guardant",
        "server_address": "127.0.0.1",
        "license_id": "0x92683BEA"
    },
    "description":"License vendor config",
    "name":"LICENSE_VENDOR",
    "tags":[]
},

Save the file.

If the license is activated using the Guardant key, then three parameters “vendor”, “server_address” and “license_id” must be specified. If you want to change the Guardant protection to HASP, then you need to delete the “license_id” field.

Launch InfluxDB OSS 2 container

InfluxDB 2.0.8-alpine is required to monitor the minimum required LP services (for more information, see the “Monitoring” section in the LUNA PLATFORM administrator manual).

Note! If you already have InfluxDB 2.0.8-alpine installed, skip this step.

Use the docker run command with these parameters:

docker run \
-e DOCKER_INFLUXDB_INIT_MODE=setup \
-e DOCKER_INFLUXDB_INIT_BUCKET=luna_monitoring \
-e DOCKER_INFLUXDB_INIT_USERNAME=luna \
-e DOCKER_INFLUXDB_INIT_PASSWORD=password \
-e DOCKER_INFLUXDB_INIT_ORG=luna \
-e DOCKER_INFLUXDB_INIT_ADMIN_TOKEN=kofqt4Pfqjn6o0RBtMDQqVoJLgHoxxDUmmhiAZ7JS6VmEnrqZXQhxDhad8AX9tmiJH6CjM7Y1U8p5eSEocGzIA== \
-v /etc/localtime:/etc/localtime:ro \
-v /var/lib/fs/fs-current/example-docker/influx:/var/lib/influxdb2 \
--restart=always \
--detach=true \
--network=host \
--name influxdb \
dockerhub.visionlabs.ru/luna/influxdb:2.0.8-alpine

Launch PostgreSQL container

If you already have PostgreSQL installed, skip this step.

Use the following command to launch PostgreSQL.

docker run \
--env=POSTGRES_USER=luna \
--env=POSTGRES_PASSWORD=luna \
--shm-size=1g \
-v /var/lib/fs/fs-current/example-docker/postgresql/data/:/var/lib/postgresql/data/ \
-v /var/lib/fs/fs-current/example-docker/postgresql/entrypoint-initdb.d/:/docker-entrypoint-initdb.d/ \
-v /etc/localtime:/etc/localtime:ro \
--name=postgres \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/postgis-vlmatch:12

-v /var/lib/luna/current/example-docker/postgresql/data/:/var/lib/postgresql/data/ - the volume command enables you to mount the “data” folder to the PostgreSQL container. The folder on the server and the folder in the container will be synchronized. The PostgreSQL data from the container will be saved to this directory.

--network=host - if you need to change the port for PostgreSQL, you should change this string to -p 5440:5432. Where the first port 5440 is the local port and 5432 is the port used inside the container.

Prepare LUNA Configurator database

Create LUNA Configurator database

Create a database for LUNA Configurator:

docker exec -i postgres psql -U luna -c "CREATE DATABASE luna_configurator;"

Allow the user to log in to the database:

docker exec -i postgres psql -U luna -c "GRANT ALL PRIVILEGES ON DATABASE luna_configurator TO luna;"

Initialize LUNA Configurator database

Use the docker run command with these parameters to create the Configurator database tables.

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /var/lib/fs/fs-current/extras/conf/configurator_configs/luna_configurator_postgres.conf:/srv/luna_configurator/configs/config.conf \
-v /var/lib/fs/fs-current/extras/conf/configurator_configs/platform_settings.json:/srv/luna_configurator/used_dumps/platform_settings.json \
-v /tmp/logs/configurator:/srv/logs \
--network=host \
--rm \
--entrypoint bash \
dockerhub.visionlabs.ru/luna/luna-configurator:v.2.1.27 \
-c "python3 ./base_scripts/db_create.py; cd /srv/luna_configurator/configs/configs/; python3 -m configs.migrate --config /srv/luna_configurator/configs/config.conf head; cd /srv; python3 ./base_scripts/db_create.py --dump-file /srv/luna_configurator/used_dumps/platform_settings.json"

/var/lib/fs/fs-current/extras/conf/configurator_configs/platform_settings.json - enables you to specify the path to the dump file with LP configurations.

./base_scripts/db_create.py; - creates database structure.

python3 -m configs.migrate head; - performs settings migrations in Configurator DB and sets revision for migration.

--dump-file /srv/luna_configurator/used_dumps/platform_settings.json - updates settings in the Configurator DB with values from the provided file.

Launch LUNA Configurator container

Use the docker run command with these parameters to launch Configurator:

docker run \
--env=PORT=5070 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /var/lib/fs/fs-current/extras/conf/configurator_configs/luna_configurator_postgres.conf:/srv/luna_configurator/configs/config.conf \
-v /tmp/logs/configurator:/srv/logs \
--name=luna-configurator \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-configurator:v.2.1.27 

LUNA Licenses service

Specify license settings using Configurator

Follow the steps below to set the settings for HASP-key or Guardant-key.

Note. Do not perform the steps described below if you have already specified the license settings in the sections “Specify HASP license settings using dump file” or “Specify Guardant license settings using dump file”.

Specify HASP license settings

Note. Perform these actions only if the HASP key is used. See the “Specify Guardant license settings” section if the Guardant key is used.

To set the license server address, follow these steps:

If the license is activated using the HASP key, then two parameters “vendor” and “server_address” must be specified. If you want to change the HASP protection to Guardant, then you need to add the “license_id” field.

Specify Guardant license settings

Note. Perform these actions only if the Guardant key is used. See the “Specify HASP license settings” section if the HASP key is used.

To set the license server address, follow these steps:

If the license is activated using the Guardant key, then three parameters “vendor”, “server_address” and “license_id” must be specified. If you want to change the Guardant protection to HASP, then you need to delete the “license_id” field.

Launch LUNA Licenses container

Use the following command to launch the service:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5120 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/licenses:/srv/logs \
--name=luna-licenses \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-licenses:v.0.7.4

Launch FaceStream

There are two ways to launch FaceStream - manual and automatic using a Docker Compose script.

Use the hyperlinks below to go to the instructions for the required launch method:

Launch FaceStream manually

Note. Perform these actions only if you are going to launch FaceStream manually. If you are going to launch FaceStream using the Docker Compose script, go to “Launch FaceStream using Docker Compose”.

Upload settings to LUNA Configurator

The main settings of LUNA Streams and FaceStream should be set in the Configurator service after its launch. The exception is the FaceEngine settings, which are set in the configuration file “faceengine.conf” and transferred during the launch of the FaceStream container.

If necessary, you can use configuration files instead of the Configurator service settings and transfer them during container launching (for more information, see the “Use FaceStream with configuration files” section of the administrator manual).

FaceStream and LUNA Streams settings are uploaded into the Configurator in different ways.

Upload LUNA Streams settings

To upload LUNA Streams settings into the Configurator service, you should use the configuration migration mechanism.

docker run \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/streams:/srv/logs \
--rm \
--network=host \
dockerhub.visionlabs.ru/luna/streams-configs:v.0.6.13 \
python3 -m streams_configs.migrate head --config_db_url postgres://luna:luna@127.0.0.1:5432/luna_configurator

--config_db_url postgres://luna:luna@127.0.0.1:5432/luna_configurator - luna_configurator database address flag

Upload FaceStream settings

FaceStream settings are located in a special file “facestream_dump.json”. To upload these settings into the Configurator service, you should use the “load_dump.py” script.

The “load_dump.py” script requires Python version 2.x or 3.x. If version 2.x is installed, then the script should be run using the python command. If version 3.x is installed, then the script should be run using the python3 command.

cd /var/lib/fs/fs-current/example-docker/luna_configurator/dumps/
python3 -m load_dump --dump-file=facestream_dump.json --luna-config=http://127.0.0.1:5070/1

All necessary parameters will be automatically added to the Configurator service.

Prepare LUNA Streams database

To launch FaceStream, you need to launch the LUNA Streams service by creating and initializing a database for it. This service is not included in the LUNA PLATFORM 5 distribution, so it should be launched separately.

Create LUNA Streams database

Create a database for LUNA Streams:

docker exec -i postgres psql -U luna -c "CREATE DATABASE luna_streams;"

Allow the user to log in to the database:

docker exec -i postgres psql -U luna -c "GRANT ALL PRIVILEGES ON DATABASE luna_streams TO luna;"

Activate PostGIS:

docker exec -i postgres psql -U luna luna_streams -c "CREATE EXTENSION postgis;"

Initialize LUNA Streams database

Initialize the data in the LUNA Streams database:

docker run -v /etc/localtime:/etc/localtime:ro \
--rm \
--network=host \
-v /tmp/logs/streams:/srv/logs \
dockerhub.visionlabs.ru/luna/luna-streams:v.0.6.13 \
python3 ./base_scripts/db_create.py --luna-config http://localhost:5070/1

Launch LUNA Streams container

The container is launched with the following command:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
--env=PORT=5160 \
--env=WORKER_COUNT=1 \
--env=RELOAD_CONFIG=1 \
--env=RELOAD_CONFIG_INTERVAL=10 \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/streams:/srv/logs \
--name=luna-streams \
--restart=always \
--detach=true \
--network=host \
dockerhub.visionlabs.ru/luna/luna-streams:v.0.6.13

To check if the service started correctly, you can perform a GET request http://127.0.0.1:5160/version. The response should return the LUNA Streams version v.0.6.13.

Launch FaceStream container

Launch FaceStream container using CPU

The container is launched as follows:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
-v /var/lib/fs/fs-current/extras/conf/configs/faceengine.conf:/srv/facestream/data/faceengine.conf \
-v /var/lib/fs/fs-current/extras/conf/configs/runtime.conf:/srv/facestream/data/runtime.conf \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/facestream:/srv/logs \
--env=PORT=34569 \
--detach=true \
--restart=always \
--name=facestream \
--network=host \
dockerhub.visionlabs.ru/luna/facestream:v.5.1.27

For a description of the remaining parameters and launching keys, see the “Launching keys” section.

To verify that the application was launched correctly, you can perform a GET request http://127.0.0.1:34569/version. The response should return the FaceStream {{Version_of_FS}}.

Launch FaceStream container using GPU

Note. Use this command only if you are going to use FaceStream with GPU.

Before launching FaceStream in GPU mode, additional dependencies should be installed (see “Install GPU dependencies” section).

Before starting the FaceStream container with GPU, it is required to enable GPU for calculations in the FaceStream settings using the “enable_gpu_processing” parameter (see the “FaceStream configuration” section in the administrator manual).

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
-v /var/lib/fs/fs-current/extras/conf/configs/faceengine.conf:/srv/facestream/data/faceengine.conf \
-v /var/lib/fs/fs-current/extras/conf/configs/runtime.conf:/srv/facestream/data/runtime.conf \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/facestream:/srv/logs \
--env=PORT=34569 \
--gpus device=0 \
--detach=true \
--restart=always \
--name=facestream \
--network=host \
dockerhub.visionlabs.ru/luna/facestream:v.5.1.27

--gpus device=0 - the parameter specifies the used GPU device and enables GPU utilization. A single GPU can be utilized per FaceStream instance. Multiple GPU utilization per instance is not available.

For a description of the remaining parameters and launching keys, see the “Launching keys” section.

To verify that the application was launched correctly, you can perform a GET request http://127.0.0.1:34569/version. The response should return the FaceStream {{Version_of_FS}}.

Launch FaceStream using Docker Compose

The Docker Compose script:

See the “docker-compose.yml” file and other files in the “example-docker” directory for the information about launched services and performed actions.

You can write your scenario that deploys and configures all the required services. This document does not include information about scenario creation or tutorial for Docker usage. Please refer to the Docker documentation to find more information about Docker and Docker Compose:

https://docs.docker.com

Launch FaceStream command

Go to the Docker Compose folder:

cd /var/lib/fs/fs-current/example-docker

Make sure that FS container are not launched before executing the script. An error will occur if you try to run a container with the same name as an existing container. If one or several LP containers are launched, you should stop them using the docker container rm -f <container_name> command. To stop all the containers, use docker container rm -f $(docker container ls -aq).

To launch FaceStream with GPU using Docker Compose, you need to follow the steps in “Install GPU dependencies”.

Launch Docker Compose:

You should be logged in the VisionLabs registry (see section “Login to registry”)

./start_facestream.sh --configurator_address=127.0.0.1

--configurator_address=127.0.0.1 - LUNA Configurator service address

Check the state of launched Docker containers.

docker ps

The list of streams is available at http://127.0.0.1:34569/api/1/streams/. Viewing the stream in the browser is available at http://127.0.0.1:34569/api/1/streams/preview/<stream_id>.

Next actions

To work further with FaceStream, you need to create a stream using a POST request “create stream” to the LUNA Streams service. The stream contains policies for processing a video stream/video file/set of images. If the stream is created with the status “pending” (by default), then the FaceStream worker will automatically start processing the stream.

See “Interaction of FaceStream with LUNA Streams” in the administrator manual for details on working with streams and LUNA Streams.

The list of streams to process is available at http://127.0.0.1:34569/api/1/streams/. Previewing a stream in a browser is available at http://127.0.0.1:34569/api/1/streams/preview/<stream_id>.

Additional information

This section provides the following additional information:

Docker commands

Show containers

To show the list of launched Docker containers use the command:

docker ps

To show all the existing Docker containers use the command:

docker ps -a 

Copy files to container

You can transfer files into the container. Use the docker cp command to copy a file into the container.

docker cp <file_location> <container_name>:<folder_inside_container>

Enter container

You can enter individual containers using the following command:

docker exec -it <container_name> bash

To exit the container, use the command:

exit

Images names

You can see all the names of the images using the command

docker images

Show container logs

You can view the container logs with the following command:

docker logs <container_name>

Delete image

If you need to delete an image:

docker rmi -f 61860d036d8c

Delete all the existing images:

docker rmi -f $(docker images -q)

Stop container

You can stop the container using the command:

docker stop <container_name>

Stop all the containers:

docker stop $(docker ps -a -q)

Delete container

If you need to delete a container:

docker container rm -f 23f555be8f3a

Delete all the containers:

docker container rm -f $(docker container ls -aq)

Launching keys

To launch FaceStream with Configurator, the keys are set using environment variables:

--env= - this parameter sets the environment variables required to start the container. The following basic values are specified:

You can set multiple values for “STREAMS_NAME”, “STREAMS_ID”, “GROUPS_NAME” and “GROUPS_ID” tags. Syntax example: --env=STREAMS_ID="037f3196-c874-4eca-9d7c-91fd8dfc9593 4caf7cf7-dd0d-4ad5-a35e-b263e742e28a"

--device= - this parameter is required to specify the address to the USB device. The address must be specified in the stream source when it is created. Example: --device=/dev/video0.

See how FaceStream works with LUNA Configurator in the section “Use FaceStream with LUNA Configurator” of the administrator manual.

Description of container launch parameters

docker run - command to launch the selected image as a new container.

-v - enables you to load the contents of the server folder into the volume of the container. This way the content is synchronized.

-v /var/lib/fs/fs-current/extras/conf/configs/faceengine.conf:/srv/facestream/data/faceengine.conf \ - this parameter enables you to use the FaceEngine settings from the configuration file “faceengine.conf”.

-v /var/lib/fs/fs-current/extras/conf/configs/runtime.conf:/srv/facestream/data/runtime.conf \ - this parameter enables you to mount the runtime configuration file into the FaceStream container. Before changing the default settings, you need to consult with VisionLabs specialists.

--network=host - this parameter specifies that there is no network simulation and a server network is used. If you need to change the port for third-party containers, replace this line with -p 5440:5432. Here, the first port 5440 is the local port, and 5432 is the port used in the container.

/etc/localtime:/etc/localtime:ro - sets the current time zone used by the container system.

--name=facestream - this parameter specifies the name of the container to be launched. The name must be unique. If a container with the same name already exists, an error will occur.

--restart=always - this parameter defines the restart policy. Daemon always restarts the container regardless of the completion code.

--detach=true - running the container in the background.

Logging to server

To enable saving logs to the server, you should:

In the Docker Compose script, synchronization of directories with folders is not configured. You need to manually add folder mounting to the docker-compose.yml file.

Create logs directory

Below are examples of commands for creating directories for saving logs and assigning rights to them for LUNA Streams and FaceStream.

mkdir -p /tmp/logs/configurator /tmp/logs/licenses /tmp/logs/facestream /tmp/logs/streams 
chown -R 1001:0 /tmp/logs/configurator /tmp/logs/licenses /tmp/logs/facestream /tmp/logs/streams

Logging activation

LUNA Streams and other LP services logging activation

To enable logging to file, you need to set the log_to_file and folder_with_logs settings in the <SERVICE_NAME>_LOGGER section of the settings for each service.

Go to the Configurator service interface (127.0.0.1:5070) and set the logs path in the container in the folder_with_logs parameter for all services whose logs need to be saved. For example, you can use the path /srv/logs.

Set the log_to_file option to true to enable logging to file.

FaceStream logging activation

To enable logging to file, you need to set the value of the logging > mode setting in the FACE_STREAM_CONFIG section to l2f (output logs only to file) or `l2b’ (output logs both to file and to console).

Go to the Configurator service interface (127.0.0.1:5070) and specify the required setting value. The location path of logs in the FaceStream container cannot be changed. It is necessary to specify the path /srv/logs when mounting.

By default, only system warnings are displayed in the FaceStream logs. By setting the “severity” parameter, you can enable error output (see the parameter description in the administrator manual).

Configurator service logging activation

The Configurator service settings are not located in the Configurator user interface, they are located in the following file:

/var/lib/luna/current/example-docker/luna_configurator/configs/luna_configurator_postgres.conf

You should change the logging parameters in this file before starting the Configurator service or restart it after making changes.

Set the path to the logs location in the container in the FOLDER_WITH_LOGS = ./ parameter of the file. For example, FOLDER_WITH_LOGS = /srv/logs.

Set the log_to_file option to true to enable logging to a file.

Mounting directories with logs when starting services

The log directory is mounted with the following argument when starting the container:

-v <server_logs_folder>:<container_logs_folder> \

where <server_logs_folder> is the directory created in the create logs directory step, and <container_logs_folder> is the directory created in the activate logging step.

Example of command to launch the FaceStream with mounting a directory with logs:

docker run \
--env=CONFIGURATOR_HOST=127.0.0.1 \
--env=CONFIGURATOR_PORT=5070 \
-v /var/lib/fs/fs-current/extras/conf/configs/faceengine.conf:/srv/facestream/data/faceengine.conf \
-v /var/lib/fs/fs-current/extras/conf/configs/runtime.conf:/srv/facestream/data/runtime.conf \
-v /etc/localtime:/etc/localtime:ro \
-v /tmp/logs/facestream:/srv/logs \
--env=PORT=34569 \
--detach=true \
--restart=always \
--name=facestream \
--network=host \
dockerhub.visionlabs.ru/luna/facestream:v.5.1.27

Examples of manual container launch commands contain these arguments.

Docker log rotation

To limit the size of logs generated by Docker, you can set up automatic log rotation. To do this, add the following data to the /etc/docker/daemon.json file:

{
    "log-driver": "json-file",
    "log-opts": {
        "max-size": "100m",
        "max-file": "5"
    }
}

This will allow Docker to store up to 5 log files per container, with each file being limited to 100MB.

After changing the file, you need to restart Docker:

systemctl reload docker

The above changes are the default for any newly created container, they do not apply to already created containers.